code: def test_initialize_pretrained(self):
      net = AHNet(
          spatial_dims=3,
          upsample_mode="transpose",
          in_channels=2,
          out_channels=3,
          psp_block_num=2,
          pretrained=True,
          progress=True,
      ).to(device)
      input_data = torch.randn(2, 2, 32, 32, 64).to(device)
      with eval_mode(net):
          result = net.forward(input_data)
          self.assertEqual(result.shape, (2, 3, 32, 32, 64))
  
imports: from __future__ import annotations
  import unittest
  import torch
  from parameterized import parameterized
  from monai.networks import eval_mode
  from monai.networks.blocks import FCN, MCFCN
  from monai.networks.nets import AHNet
  from tests.utils import skip_if_quick, test_pretrained_networks, test_script_save
label_desc: Use image node_id 2 for calling the TestAHNETWithPretrain obj's underlying member method code with example usage: obj.test_initialize_pretrained() without return types